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Abstract Carotenoid pigments are the basis for much red, orange, and yellow coloration in nature and central to visual signaling. However, as pigment concentration increases, carotenoid signals not only darken and become more saturated but they also redshift; for example, orange pigments can look red at higher concentration. This occurs because light experiences exponential attenuation, and carotenoid‐based signals have spectrally asymmetric reflectance in the visible range. Adding pigment disproportionately affects the high‐absorbance regions of the reflectance spectra, which redshifts the perceived hue. This carotenoid redshift is substantial and perceivable by animal observers. In addition, beyond pigment concentration, anything that increases the path length of light through pigment causes this redshift (including optical nano‐ and microstructures). For example, maleRamphocelustanagers appear redder than females, despite the same population and concentration of carotenoids, due to microstructures that enhance light–pigment interaction. This mechanism of carotenoid redshift has sensory and evolutionary consequences for honest signaling in that structures that redshift carotenoid ornaments may decrease signal honesty. More generally, nearly all colorful signals vary in hue, saturation, and brightness as light–pigment interactions change, due to spectrally asymmetrical reflectance within the visible range of the relevant species. Therefore, the three attributes of color need to be considered together in studies of honest visual signaling. 

Abstract The increasing availability of genomic resequencing data sets and high-quality reference genomes across the tree of life present exciting opportunities for comparative population genomic studies. However, substantial challenges prevent the simple reuse of data across different studies and species, arising from variability in variant calling pipelines, data quality, and the need for computationally intensive reanalysis. Here, we present snpArcher, a flexible and highly efficient workflow designed for the analysis of genomic resequencing data in nonmodel organisms. snpArcher provides a standardized variant calling pipeline and includes modules for variant quality control, data visualization, variant filtering, and other downstream analyses. Implemented in Snakemake, snpArcher is user-friendly, reproducible, and designed to be compatible with high-performance computing clusters and cloud environments. To demonstrate the flexibility of this pipeline, we applied snpArcher to 26 public resequencing data sets from nonmammalian vertebrates. These variant data sets are hosted publicly to enable future comparative population genomic analyses. With its extensibility and the availability of public data sets, snpArcher will contribute to a broader understanding of genetic variation across species by facilitating the rapid use and reuse of large genomic data sets. 

Abstract Coral reef fishes constitute one of the most diverse assemblages of vertebrates on the planet. Color patterns are known to serve a number of functions including intra- and inter-specific signaling, camouflage, mimicry, and defense. However, the relative importance of these and other factors in shaping color pattern evolution is poorly understood. Here we conduct a comparative phylogenetic analysis of color pattern evolution in the butterflyfishes (Chaetodontidae). Using recently developed tools for quantifying color pattern geometry as well as machine learning approaches, we investigate the tempo of evolution of color pattern elements and test whether ecological variables relating to defense, depth, and social behavior predict color pattern evolution. Butterflyfishes exhibit high diversity in measures of chromatic conspicuousness and the degrees of fine versus gross scale color patterning. Surprisingly, most diversity in color pattern was not predicted by any of the measures of ecology in our study, although we did find a significant but weak relationship between the level of fine scale patterning and some aspects of defensive morphology. We find that the tempo of color pattern diversification in butterflyfishes has increased toward the present and suggest that rapid evolution, presumably in response to evolutionary pressures surrounding speciation and lineage divergence, has effectively decoupled color pattern geometry from some aspects of ecology. Machine learning classification of color pattern appears to rely on a set of features that are weakly correlated with current color pattern geometry descriptors, but that may be better suited for the detection of discrete components of color pattern. A key challenge for future studies lies in determining whether rapid evolution has generally decoupled color patterns from ecology, or whether convergence in function produces convergence in color pattern at phylogenetic scales.